A projected process kriging algorithm for sensor networks with heterogeneous error characteristics

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Abstract

Large monitoring networks are becoming increasingly common and can generate large datasets from thousands to millions of observations in size, often with high temporal resolution. Processing large datasets using traditional geostatistical methods is prohibitively slow and in real world applications different types of sensor can be found across a monitoring network. Heterogeneities in the error characteristics of different sensors, both in terms of distribution and magnitude, presents problems for generating coherent maps. An assumption in traditional geostatistics is that observations are made directly of the underlying process being studied and that the observations are contaminated with Gaussian errors. Under this assumption, sub–optimal predictions will be obtained if the error characteristics of the sensor are effectively non–Gaussian. One method, model based geostatistics, assumes that a Gaussian process prior is imposed over the (latent) process being studied and that the sensor model forms part of the likelihood term. One problem with this type of approach is that the corresponding posterior distribution will be non–Gaussian and computationally demanding as Monte Carlo methods have to be used. An extension of a sequential, approximate Bayesian inference method enables observations with arbitrary likelihoods to be treated, in a projected process kriging framework which is less computationally intensive. The approach is illustrated using a simulated dataset with a range of sensor models and error characteristics.

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Details

Original languageEnglish
StateUnpublished - 2008
Event8th International Geostatistics Congress - Santiago , Chile

Conference

Conference8th International Geostatistics Congress
Abbreviated titleGeostats 2008
CountryChile
CitySantiago
Period1/12/085/12/08

Bibliographic note

Proceedings: Ortiz, Julián M.; Emery, Xavier (eds). GEOSTATS 2008 – VIII International Geostatistics Congress, 1-5 December, Santiago, Chile. Mining Engineering Department, University of Chile. Two volumes, 1188 pages.

    Keywords

  • large monitoring networks, model based geostatistics, Gaussian process prior, Monte Carlo methods, approximate Bayesian inference method

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